Difference between revisions of "Part:BBa K098995:Experience"

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===Applications of BBa_K098995===
 
===Applications of BBa_K098995===
  
===User Reviews===
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<div id="blueBox"><p>High Temperature Induced System – cI Promoter & cI repressor</p></div>
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<div id="Box"><h2> Modeling and simulations of high temperature induced device BBa_K098995 – cI promoter & cI repressor
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<partinfo>BBa_K098995 </partinfo>
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<I>Kun Xue</I>
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In order to characterize the thermoinducible promoter properly, we have used 3 sets of control cells (not shown in graph): <br>
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*Positive control cells: Containing the BBa_I13522, acting as a baseline comparison by constitutively expressing GFP.
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*Negative control cells: These contain the thermoinducible promoter on its own ( BBa_K098995) with no GFP attached to it. These serve as cells without any fluorescence.
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*Cells shifted from 28 to 42 degrees. This characterises the transition stage.
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<p> In order to characterize this high temperature induced device <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a>, the fluorescence intensity of<a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> is measured by the flow cytometry (Figure. 1).
[[Image:II09_Thermoconstruct_main2.jpg|500px]]<br>
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<div><img src = https://static.igem.org/mediawiki/2011/e/e4/M-1.1.JPG></div>
The characterization of the thermoinducible promoter is as follows:<br>  
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<br><b>Figure1.</b> Part <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> Design. The heat induced device  <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a>uses gene <a href=" https://parts.igem.org/Part:BBa_K098997">BBa_K098997</a> coding for cI repressor to inhibit the cI promoter <a href=" https://parts.igem.org/Part:BBa_R0051">BBa_R0051</a>. The activity of cI repressor is decreased by elevating temperature from 30 ℃ to 42 ℃.
*At 28 ºC, there is no fluorescence (GFP) output because its expression is repressed
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A differential equation is used to calculate protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a>  as follows.<br><br>
*At 42 ºC, we can observe an increase in fluorescence (GFP) output,as its expression is no longer repressed
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Hence, we can conclude that an increase in temperature de-represses the expression of the thermoinducible system.<br>
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As part of Imperial iGEM 2009 [http://2009.igem.org/Team:Imperial_College_London| <i>The E.ncapsulator</i>] project, we characterised this part using the composite [https://parts.igem.org/Part:BBa_K200022| BBa_K200022].<br>
 
  
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The 2011 iGEM Team of Freiburg tried to use this part. Sequencing showed several inconsistencies compared to the submitted sequence. Especially between the RBS and the start codon of the repressor 6 bases are missing, probably leading to a diminished expression of the repressor. 
 
  
Freigem added the missing nts between RBS and cI by QuickChange PCR. The functionality of the corrected part is currently under investigation. The '''new''' annotated sequence can be downloaded [https://static.igem.org/mediawiki/2011/e/e5/Freiburg11_Corrected_K098995.gb here].
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<div><img src = "https://static.igem.org/mediawiki/2011/e/e3/M-5.JPG" width="450"></div>
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<p> This equation describes the concentration of GFP in <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>change with time (Figure. 1). Alpha-Temp is the protein expression rates corresponding to <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a>which is a temperature sensitive expression device. To describe transition during log phase and stationary phase, the alpha-Temp is assumed to zero in stationary phase. Gamma-GFP are decay rates of the GFP proteins. When bacteria divide, the molecular in a bacterium will be dilute. Because bacteria grow faster, the dilution rate d(t) is included in this model and can be calculated from OD ratio of medium (Figure. 2). The values of the kinetic parameters used in the simulation were initially obtained from the literature and experimental data. Data computations were performed with Matlab software. A program was written and used as a subroutine in Matlab for parameter optimization using nonlinear regression (Figure. 3).</p>
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<div><img src = "https://static.igem.org/mediawiki/2011/e/e8/M-2.jpg" width="450"></div>
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<br><b>Figure 2. </b> The OD ratio is increased faster in log phase than it in stationary phase. The dilution rate d(t) can be calculated from OD ratio and used in out model.
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<div><img src = "https://static.igem.org/mediawiki/2011/c/c2/M-3.jpg" width="450"></div>
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<br><b>Figure 3. </b>The behavior of high temperature induced device <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> at 25°C, 37 °C and 42°C. Experimental data (dot) and simulated results (line) of the model  suggest this temperature-dependent device can control the expression level of the target protein by the host cell’s incubation. The fitting results indicate our dynamic model can quantitatively assess the protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>during log phase and stationary phase.
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<p><br>Using least squares estimation from experimental data, the relative the protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>  at 25°C, 37 °C and 42°C were estimated (Figure. 4).<br></p>
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<div><img src = "https://static.igem.org/mediawiki/2011/b/b2/M-4.JPG" width="450"></div>
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<br><b>Figure 4. </b>The relative the protein expression activity of  <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>at 25°C, 37 °C and 42°C estimated using least squares estimation from experimental data. The protein expression activity at 42°C is higher than 25°C, 37 °C
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<p><br>According to the fitting results (Figure. 3), the dynamic model successfully approximated the behavior of our high-temperature induced system. The model equation presents interesting mathematical properties that can be used to explore how qualitative features of the genetic circuit depend on reaction parameters. This method of dynamic modeling can be used to guide the choice of genetic ‘parts’ for implementation in circuit design in the future.</p><br>
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<br><b>References </b>
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<p>Alon, U. (2007) An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC.</p>
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Revision as of 14:55, 5 October 2011

This experience page is provided so that any user may enter their experience using this part.
Please enter how you used this part and how it worked out.

Applications of BBa_K098995



High Temperature Induced System – cI Promoter & cI repressor

Modeling and simulations of high temperature induced device BBa_K098995 – cI promoter & cI repressor

In order to characterize this high temperature induced device <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a>, the fluorescence intensity of<a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> is measured by the flow cytometry (Figure. 1).

<img src = M-1.1.JPG>


Figure1. Part <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> Design. The heat induced device <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a>uses gene <a href=" https://parts.igem.org/Part:BBa_K098997">BBa_K098997</a> coding for cI repressor to inhibit the cI promoter <a href=" https://parts.igem.org/Part:BBa_R0051">BBa_R0051</a>. The activity of cI repressor is decreased by elevating temperature from 30 ℃ to 42 ℃. A differential equation is used to calculate protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a> as follows.


<img src = "M-5.JPG" width="450">
<p> This equation describes the concentration of GFP in <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>change with time (Figure. 1). Alpha-Temp is the protein expression rates corresponding to <a href=" https://parts.igem.org/Part:BBa_K098995">BBa_K098995</a>which is a temperature sensitive expression device. To describe transition during log phase and stationary phase, the alpha-Temp is assumed to zero in stationary phase. Gamma-GFP are decay rates of the GFP proteins. When bacteria divide, the molecular in a bacterium will be dilute. Because bacteria grow faster, the dilution rate d(t) is included in this model and can be calculated from OD ratio of medium (Figure. 2). The values of the kinetic parameters used in the simulation were initially obtained from the literature and experimental data. Data computations were performed with Matlab software. A program was written and used as a subroutine in Matlab for parameter optimization using nonlinear regression (Figure. 3).

<img src = "M-2.jpg" width="450">


Figure 2. The OD ratio is increased faster in log phase than it in stationary phase. The dilution rate d(t) can be calculated from OD ratio and used in out model.

<img src = "M-3.jpg" width="450">


Figure 3. The behavior of high temperature induced device <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> at 25°C, 37 °C and 42°C. Experimental data (dot) and simulated results (line) of the model suggest this temperature-dependent device can control the expression level of the target protein by the host cell’s incubation. The fitting results indicate our dynamic model can quantitatively assess the protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>during log phase and stationary phase.


Using least squares estimation from experimental data, the relative the protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a> at 25°C, 37 °C and 42°C were estimated (Figure. 4).

<img src = "M-4.JPG" width="450">


Figure 4. The relative the protein expression activity of <a href=" https://parts.igem.org/Part:BBa_K098988">BBa_K098988</a>at 25°C, 37 °C and 42°C estimated using least squares estimation from experimental data. The protein expression activity at 42°C is higher than 25°C, 37 °C


According to the fitting results (Figure. 3), the dynamic model successfully approximated the behavior of our high-temperature induced system. The model equation presents interesting mathematical properties that can be used to explore how qualitative features of the genetic circuit depend on reaction parameters. This method of dynamic modeling can be used to guide the choice of genetic ‘parts’ for implementation in circuit design in the future.



References

Alon, U. (2007) An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC.